Literature DB >> 32657779

Genomic landscape of metastatic breast cancer identifies preferentially dysregulated pathways and targets.

Matt R Paul1,2,3, Tien-Chi Pan1,2,3, Dhruv K Pant1,2,3, Natalie Nc Shih1,4, Yan Chen1,2,3, Kyra L Harvey1,2,3, Aaron Solomon1,2,3, David Lieberman4, Jennifer Jd Morrissette4, Danielle Soucier-Ernst1,5, Noah G Goodman1,5, S William Stavropoulos1,6, Kara N Maxwell1,5, Candace Clark1,5, George K Belka1,2,3, Michael Feldman1,4, Angela DeMichele1,5,7, Lewis A Chodosh1,2,3,5.   

Abstract

Nearly all breast cancer deaths result from metastatic disease. Despite this, the genomic events that drive metastatic recurrence are poorly understood. We performed whole-exome and shallow whole-genome sequencing to identify genes and pathways preferentially mutated or copy-number altered in metastases compared with the paired primary tumors from which they arose. Seven genes were preferentially mutated in metastases - MYLK, PEAK1, SLC2A4RG, EVC2, XIRP2, PALB2, and ESR1 - 5 of which are not significantly mutated in any type of human primary cancer. Four regions were preferentially copy-number altered: loss of STK11 and CDKN2A/B, as well as gain of PTK6 and the membrane-bound progesterone receptor, PAQR8. PAQR8 gain was mutually exclusive with mutations in the nuclear estrogen and progesterone receptors, suggesting a role in treatment resistance. Several pathways were preferentially mutated or altered in metastases, including mTOR, CDK/RB, cAMP/PKA, WNT, HKMT, and focal adhesion. Immunohistochemical analyses revealed that metastases preferentially inactivate pRB, upregulate the mTORC1 and WNT signaling pathways, and exhibit nuclear localization of activated PKA. Our findings identify multiple therapeutic targets in metastatic recurrence that are not significantly mutated in primary cancers, implicate membrane progesterone signaling and nuclear PKA in metastatic recurrence, and provide genomic bases for the efficacy of mTORC1, CDK4/6, and PARP inhibitors in metastatic breast cancer.

Entities:  

Keywords:  Bioinformatics; Breast cancer; Genetics; Oncology

Mesh:

Substances:

Year:  2020        PMID: 32657779      PMCID: PMC7410083          DOI: 10.1172/JCI129941

Source DB:  PubMed          Journal:  J Clin Invest        ISSN: 0021-9738            Impact factor:   19.456


  83 in total

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  32 in total

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